Aktuelle Publikationen

This article analyses the role of institutional misfit in why member states customise European Union (EU) renewable energy (RE) policies when implementing them. Institutional misfit theory posits that member states only adjust to EU policies when the adaptation pressure remains moderate and national actors’ policy preferences align. Conversely, this article tests the argument that member states manage institutional misfit by adjusting – customising – EU policies, that is, through vertical EU policy change rather than domestic change. Using Qualitative Comparative Analysis, the article compares the customisation of EU Directive 2009/28/EC in Austria, France, Germany, the Netherlands, Sweden and the United Kingdom. Surprisingly, results suggest that institutional misfit is not a necessary condition for customised implementation. Instead, when high institutional fit meets high salience, member states may issue substantively more ambitious policies than the EU requires. Conversely, when high institutional fit meets low salience, member states have no impetus to customise EU rules.

Purpose
Globally, many children are exposed to violent discipline in multiple settings. Interventions to prevent violent discipline are therefore highly needed. In the present study, the feasibility of the intervention Interaction Competencies with Children – for Parents (ICC-P), an additional module of a school-based intervention for teachers, was tested. The intervention aims to prevent violent discipline by changing attitudes towards such method and fostering supportive adult-child interaction through non-violent interaction skills.

Methods
In total, 164 parents (Mage= 39.55, range = 24 70, 72.3% female) from four public secondary schools in Tanzania participated in a four-day training conducted by six trainers (Mage= 44.67, range = 40–47, 50% female). Using a One-Group Pre-Post design, we measured the feasibility and preliminary effectiveness of the intervention qualitatively and quantitatively. Parents were assessed via self-administered questionnaires before and six weeks after the intervention. Trainers rated the implementation of every workshop session.

Results
Based on descriptive statistics and Classical Content Analysis, implementing trainers and participants rated ICC-P as feasible. Participants indicated a high need for such interventions and showed high acceptance. They were able to integrate core aspects of the intervention in their daily interactions with children. Using t-tests, ICC-P proved to be preliminarily effective; parents reported applying less violent discipline and holding more critical attitudes about such measures after the intervention.

Conclusion
ICC-P is feasible intervention that showed initial signs of effectiveness. We recommend combining the parents’ training module with the teachers’ module to prevent violence in multiple settings.

Personal relations and networks have long been argued to dominate African politics. Since personal power is difficult to measure, much of the literature has remained either anecdotal or has used ethnicity to approximate power distributions. This article is proposing a social network approach to the analysis of personal power in legislatures and cabinets in three cases: Ghana, Togo and Gabon. We combine survey data on parliamentary discussion networks with a new data set on cabinet appointments. We find that power accumulation in one institution correlates with power accumulation in the other in all three countries, irrespective of the level of democracy: individuals build up a unique power base to advance their careers. We also find differences between the modes of power accumulation and elite integration across our cases. Our findings could stimulate new debates on personal power, regime survival and elite reproduction across different regimes.

To what extent, and under what conditions, have workfare reforms shaped public opinion towards the unemployed? This article unpacks the punitive and enabling dimensions of the workfare turn and examines how changes to the rights and obligations of the unemployed have influenced related policy preferences. To do so, it presents a novel dataset on these reforms across a diverse set of welfare states and investigates potential feedback effects by combining our data with four waves of survey data from Europe and North America. Results suggest that while enabling measures generate more lenient attitudes towards the unemployed, punitive measures have no clear effect on public opinion – but they do accentuate the gap between the preferences of high- and low-income individuals. This leads us to conclude that the trend towards punitive and enabling measures since the 1980s has not broadly undermined solidarity with the unemployed, though it has increased income-based polarization.

Economic inequality constitutes a defining challenge of our time and it remains puzzling why rising levels of inequality have not led to more redistribution. In this article a novel individual-level perspective is taken, with a focus on how much legislators and citizens agree on questions of redistribution and inequality, and what causes these mismatches. The study compares legislators’ views to a representative citizen sample in Switzerland. The results show considerable disagreement between the groups with legislators being more sceptical towards redistribution and seeing inequality as fairer outcome. The mismatch is only partially explained by legislators’ higher social status. Ideology plays a fundamental role as more polarisation according to ideological lines is found among elites and their attitudes are also more rooted in their ideology. In sum, the findings point to some underexplored angles of the puzzle of why not more redistribution has been observed and thus offer a valuable addition to the existing literature.

Objective: Providing country-level estimates for prevalence rates of Generalized Anxiety Disorder (GAD), COVID-19 exposure and food insecurity (FI) and assessing the role of persistent threats to survival—exemplified by exposure to COVID-19 and FI—for the mental health crisis in Africa.

Methods: Original phone-based survey data from Mozambique, Sierra Leone, Tanzania and Uganda (12 consecutive cross-sections in 2021; n = 23,943) were analyzed to estimate prevalence rates of GAD. Logistic regression models and mediation analysis using structural equation models identify risk and protective factors.

Results: The overall prevalence of GAD in 2021 was 23.3%; 40.2% in Mozambique, 17.0% in Sierra Leone, 18.0% in Tanzania, and 19.1% in Uganda. Both COVID-19 exposure (OR_adj. 1.4; CI 1.3–1.6) and FI (OR_adj 3.2; CI 2.7–3.8) are independent and significant predictors of GAD. Thus, the impact of FI on GAD was considerably stronger than that of COVID-19 exposure.

Conclusion: Persistent threats to survival play a substantial role for mental health, specifically GAD. High anxiety prevalence in the population requires programs to reduce violence and enhance social support. Even during a pandemic, addressing FI as a key driver of GAD should be prioritized by policymakers.

Does technological change fuel political disruption? Drawing on fine-grained labor market data from Germany, this paper examines how technological change affects regional electorates. We first show that the well-known decline in manufacturing and routine jobs in regions with higher robot adoption or investment in information and communication technology (ICT) was more than compensated by parallel employment growth in the service sector and cognitive non-routine occupations. This change in the regional composition of the workforce has important political implications: Workers trained for these new sectors typically hold progressive political values and support progressive pro-system parties. Overall, this composition effect dominates the politically perilous direct effect of automation-induced substitution. As a result, technology-adopting regions are unlikely to turn into populist-authoritarian strongholds.

Political polarization is often understood in terms of extreme issue positions. But polarization can only emerge if issue positions are aligned into a single ideological spectrum, ranging from left/ liberal to right/conservative. It is unclear how a high-dimensional space of policy issues can organize itself into a single ideological spectrum and give rise to polarization. We explain this phenomenon using Weighted Balance Theory (WBT), which describes the interaction of issue positions and interpersonal affect. By implementing WBT into an agent-based opinion dynamics model, we generate a single ideological spectrum from an arbitrarily high dimensional issue space. Furthermore, we show that WBT outperforms other models in predicting respondents’ attitudes in 44 years worth of empirical data from the ANES survey. A calibrated version of our model can reproduce properties of empirically observed opinion distributions.

Spoken administrative language is a critical element in the relationship between citizens and the state, especially when it comes to face-to-face interactions between officials and citizens during the delivery of public services. But preceding work offers little insights on the verbal features of street-level bureaucracy. Drawing on communication studies, we argue that administrative language differs along both a relational and an informational linguistic component. To test the consequentiality of this theory, we design a factorial survey experiment with a representative sample of 1,402 German citizens. Participants evaluated audio recordings of a hypothetical service encounter where we systematically varied the language used by the official and the service decision, measuring participants’ service satisfaction as the main outcome. Based on regression analysis, we find that relational elements of administrative language improve citizen satisfaction, independent of the service outcome, but that the effect does not hold for the informational component. These findings emphasize the importance of relational communication in citizen-state interactions, which tends to be neglected in public administration theory and practice.

This article studies individual-level attitudes towards long-term investment policies using novel survey data for the case of Germany. Building on a budding literature on the relationship between environmental and social policy attitudes, our first contribution to research is to show that citizens, when prompted to think about their support for long-term investment policies, support welfare state related policies such as investments in education and pensions to a greater degree than non-welfare state issues such as public infrastructure investment or renewable energy. Citizens are most supportive of using present-day redistributive policies – in our case: increasing income taxes on the rich – in order to finance long-term investment. We also find evidence that political trust is positively associated with support for long-term investment policies, but in particular investments in education and renewables. Furthermore, our analysis reveals the importance of individual political ideology. These findings have implications for public demand for tackling the long-term issues faced by society today.

With the increasing relevance of ethnic groups as political actors, the literature has attempted to identify and study the ethnic organizations representing these groups. How do these organizations use digital communication channels to reach their domestic and international audiences? To enable research on these questions, this article introduces the Ethnic Organizations Online dataset, a new data collection focusing on the online channels that ethnic organizations use. The dataset includes four types of channels: Twitter (since July 2023, rebranded by Elon Musk as X); Facebook; Instagram; and regular websites. It relies on the Ethnic Power Relations – Organizations database, and is therefore compatible with an entire family of datasets on ethnic politics. Featuring more than 2000 online channels used by 265 groups, it allows researchers to study a wide variety of questions related to digital ethnic mobilization. The article presents three examples of how the dataset can be used. We study: (a) how a group’s political goals influence social media adoption; (b) how elections impact the organizations’ communication frequency and how this differs between democracies and autocracies; and (c) how the power status of a group affects the content of their communication. We provide replication codes facilitating the use of the dataset in applied research.

While survey data has long been the focus of quantitative social science analyses, observational and content data, although long-established, are gaining renewed attention; especially when this type of data is obtained by and for observing digital content and behavior. Today, digital technologies allow social scientists to track “everyday behavior” and to extract opinions from public discussions on online platforms. These new types of digital traces of human behavior, together with computational methods for analyzing them, have opened new avenues for analyzing, understanding, and addressing social science research questions. However, even the most innovative and extensive amounts of data are hollow if they are not of high quality. But what does data quality mean for modern social science data? To investigate this rather abstract question the present study focuses on four objectives. First, we provide researchers with a decision tree to identify appropriate data quality frameworks for a given use case. Second, we determine which data types and quality dimensions are already addressed in the existing frameworks. Third, we identify gaps with respect to different data types and data quality dimensions within the existing frameworks which need to be filled. And fourth, we provide a detailed literature overview for the intrinsic and extrinsic perspectives on data quality. By conducting a systematic literature review based on text mining methods, we identified and reviewed 58 data quality frameworks. In our decision tree, the three categories, namely, data type, the perspective it takes, and its level of granularity, help researchers to find appropriate data quality frameworks. We, furthermore, discovered gaps in the available frameworks with respect to visual and especially linked data and point out in our review that even famous frameworks might miss important aspects. The article ends with a critical discussion of the current state of the literature and potential future research avenues.

Recent approaches to text analysis from social media and other corpora rely on word lists to detect topics, measure meaning, or to select relevant documents. These lists are often generated by applying computational lexicon expansion methods to small, manually curated sets of seed words. Despite the wide use of this approach, we still lack an exhaustive comparative analysis of the performance of lexicon expansion methods and how they can be improved with additional linguistic data. In this work, we present LEXpander, a method for lexicon expansion that leverages novel data on colexification, i.e., semantic networks connecting words with multiple meanings according to shared senses. We evaluate LEXpander in a benchmark including widely used methods for lexicon expansion based on word embedding models and synonym networks. We find that LEXpander outperforms existing approaches in terms of both precision and the trade-off between precision and recall of generated word lists in a variety of tests. Our benchmark includes several linguistic categories, as words relating to the financial area or to the concept of friendship, and sentiment variables in English and German. We also show that the expanded word lists constitute a high-performing text analysis method in application cases to various English corpora. This way, LEXpander poses a systematic automated solution to expand short lists of words into exhaustive and accurate word lists that can closely approximate word lists generated by experts in psychology and linguistics.